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Ionic liquid-functionalized magnetic polystyrene nanoparticles for efficient removal of methyl violet and acid red 88 from aqueous media
The effects of time-restricted feeding on early vascular, liver, and renal structural changes, oxidative stress, and inflammation in obese rats
Abstract Cardiovascular disease remains a leading global cause of death, highlighting the need for new strategies to improve cardiovascular health. Time-restricted feeding (TRF), which limits daily food intake to a specific window, has shown promise in improving metabolic health and supporting weight control. This study investigated the effects of TRF in an obese rat model induced by a high-fat diet (HFD), focusing on early vascular, liver, and kidney structural changes, as well as oxidative stress and inflammation. Thirty male Sprague Dawley rats were assigned to five groups: a normal diet group (NOR), a normal chow with TRF (NOR + TRFNC), a continued HFD group (OB), an HFD with TRF group (OB + TRFHFD), and a group switched to TRF with normal chow (OB + TRFNC). Obesity was induced in three groups over six weeks, followed by a six-week intervention phase. TRF involved fasting for 16 h daily (5:00 p.m. to 9:00 a.m.). TRF led to improved lipid profiles and atherogenic indices in obese rats, regardless of diet. Elevated liver enzymes, alanine aminotransferase (ALT), aspartate aminotransferase (AST), and alkaline phosphatase (ALP) in obese rats were normalized by TRF. Additionally, TRF increased vascular superoxide dismutase (SOD) and decreased malondialdehyde (MDA), interleukin-6 (IL-6), and tumor necrosis factor-alpha (TNF-α). Histological analysis showed that fat infiltration and steatosis in the liver were reduced by TRF. Renal and vascular structures also showed improvement. In conclusion, TRF exhibits anti-atherosclerotic effects, likely due to reduced vascular oxidative stress, inflammation, improved liver and kidney function, and better atherogenic profiles. These benefits were supported by histopathological findings in hepatic and renal tissues.
Enhanced backpropagation neural network accuracy through an improved genetic algorithm for tourist flow prediction in an ecological village
Single-Crystal-to-Single-Crystal Synthesis of an Adaptive Two-Dimensional Polymer with Dynamic Pores
Publisher Correction: Influence of acidified-biochar on phosphorus and potassium availability in alkaline sandy soil
A novel integrated framework for damage detection in steel structures using magnetic piezoelectric sensors and hybrid damage index
The effect of the use of irrigation activation methods with different file systems on the amount of apical debris extrusion
Image segmentation network based on enhanced dual encoder
Obstacle avoidance inspection method of cable tunnel for quadruped robot based on particle swarm algorithm and neural network
Persistent open chromatin state in early-life stress-activated cells of the VTA
Protective effect of tanshinone IIA against Listeria monocytogenes infection in vitro and in vivo
Contribution of channel geometry adjustments to stage variance based on rating-curves in the main stream of the Lancang–Mekong river
Abstract Extensive dam development and climate change have altered seasonal stage variation patterns, which are critical to residents in the riparian zone along the Lancang–Mekong River. However, the effects of morphological changes and reservoirs on stage variability are not clear due to data-scarce fluvial systems. In this work, discharge and water level data from five hydrological stations (1960–2020) were acquired to assess temporal shifts in rating curves during different disturbance periods. The contribution of channel geometry adjustment to the stage variance of the extreme flow regime under high- and low-flow conditions were empirical analysed by rating-curve method. Analysis revealed that the stage variance along the main channel was modulated by channel geometry adjustment under both high- and low-flow conditions, even though discharge was the dominant factor. Moreover, the degree of modulation resulting from geometric adjustment varied under different flow and reach conditions, which varied in the ranges of -0.57 ~ 0.27 m and − 0.41 ~ 0.39 m under low- and high-flow conditions, respectively. Furthermore, an inverse channel geometry adjustment response was observed for 60% of the high-flow conditions versus 40% of the low-flow conditions. The Luang Prabang–Vientiane reach was transitional in terms of the effects of channel geometry adjustment on stage variation. Our findings quantified how channel geometry adjustment modulated water levels across various extreme regimes, offering insights into the morphological processes of data-scarce river reaches.
Development, characterization and biological studies of Mn-doped ZnO/Ti biomaterials for regenerative medicine
Combined vacuum osmotic dehydration by pomegranate juice concentrate and hot-air assisted radiofrequency drying to produce fortified orange slices
Effect of intraoperative delayed time on correction accuracy in 1050 Hz excimer laser-assisted in situ keratomileusis
Robinin attenuates cardiac oxidative stress-induced endoplasmic reticulum-dependent apoptosis through AKT/GSK3β pathway
Ligand Architecture Control Geometry, Self-Sorting, and Postsynthetic Modification in Anthracene-Based Organometallic Assemblies
Influence of alkali treatment in enhancing crystallinity and breaking force of pineapple leaf fiber
Abstract The study investigates the effect of alkali treatment on pineapple leaf fiber (PALF) woven mats. The woven mat was chemically treated with varied concentrations (3, 6, and 9% w/v) of sodium hydroxide (NaOH) solutions for different exposure times (30, 60, and 90 min), making a total of 9 experiments. The structural, morphological, and chemical properties of untreated and alkali-treated specimens were investigated by using X-ray diffraction (XRD), Scanning Electron Microscopy (SEM), and Fourier Transform Infrared Spectroscopy (FTIR) respectively. The mechanical property was assessed through the breaking force analysis. The XRD result indicated that the fiber mat treated with NaOH of 6% w/v concentration exposed to 30 min yields a crystallinity index (CI) of 63.95% and a crystallite size (CS) of 7.05 nm. The FTIR analysis helped to identify chemically active groups involved in PALF and the characteristic absorption peaks associated with partial and complete removal of wax and other impurities. SEM results quantitatively indicated the elimination of amorphous components from the surfaces. The specimen with the highest CI of 63.95% exhibited the maximum breaking force of 356.92 N. The results indicate that the increase in CI and CS not only improves the mechanical properties but also helps in stronger interfacial bonding in polymer composite applications.
Incidence and severity of aortic stenosis according to machine learning predicted risk of atrial fibrillation
Abstract Atrial fibrillation (AF) and aortic stenosis (AS) are two common progressive conditions affecting older persons that share pathobiological pathways. Early detection of AS is critical for improving outcomes, but no prediction tool exists to inform decision making. In this study we evaluated the association between machine learning predicted risk of incident AF from clinical health records (using the FIND-AF algorithm) and severity and incidence of AS. In a disease registry we found that higher FIND-AF risk was correlated with parameters of increasing AS severity including smaller aortic valve area, and higher maximum velocity and peak pressure gradient but ability to differentiate severe from non-severe AS was moderate (sensitivity 0.545, specificity 0.770). In over 400,000 primary care clinical health records, FIND-AF showed good prediction performance for incident AS (AUC 0.782, 95% CI 07.69–0.795), and the cumulative incidence increased with higher FIND-AF risk strata. The hazard of AS was over 40-fold higher in patients with FIND-AF risk scores of more than 0.05 compared to patients with FIND-AF risk scores of less than 0.005. Predicted risk of AF is associated with severity and incidence of AS, but predictive ability for AS may be improved by developing a machine learning model specifically for this outcome.